- Avrupa Bilim ve Teknoloji Dergisi
- Sayı: 52
- Analysis and Evaluation of Conventional Methods for Diabetes Prediction
Analysis and Evaluation of Conventional Methods for Diabetes Prediction
Authors : Canan Batur Şahin, Hayriye Tanyildiz, Özlem Batur Dinler
Pages : 220-233
View : 45 | Download : 42
Publication Date : 2023-12-15
Article Type : Research
Abstract :Diabetes, a chronic disease that affects millions of people worldwide, is characterized by the body\'s inability to manage blood sugar levels effectively. If left unchecked or not managed properly, this condition can lead to serious consequences such as heart disease, stroke, kidney failure, and even blindness. Due to the interplay of genetic and lifestyle factors, the incidence of diabetes is increasing, positioning it as a significant global health problem requiring urgent attention. The World Health Organization (WHO) reports that the global prevalence of diabetes has nearly doubled since 1980, rising from 4.7% to 8.5% in the adult population. This increase highlights the urgency and importance of strategies aimed at early diagnosis and effective management of the disease. In the face of such a public health problem, health services seek help from technological developments to combat this epidemic. Among the most promising technological frontiers in healthcare is Machine Learning (ML), a subset of artificial intelligence (AI) that can analyze vast amounts of data, identify patterns and predict outcomes. Machine learning has the potential to revolutionize diabetes management by providing valuable insights into patient health, informing treatment decisions, and even predicting a person\'s risk of developing the disease in the future. This technology, if used properly, could change the game in the fight against diabetes. In this context, the use of traditional classifier methods to estimate diabetes risk seems to be a viable and efficient approach. As these methods continue to evolve, they play an important role in the early detection and effective treatment of this chronic disease, promising to increase the accuracy and precision of diabetes risk estimation. In this article, we will examine how traditional classifier methods are used to predict diabetes, the implications of this technology for disease diagnosis, and the future potential of this evolving field.Keywords : Diyabet, Yapay zeka, Sınıflandırıcılar, Makine Öğrenmesi, Tahmin.